A Naturalistic Study of Driving Behavior in Older Adults and Preclinical Alzheimer Disease: A Pilot Study.

A clinical consequence of symptomatic Alzheimer's disease (AD) is impaired driving performance. However, decline in driving performance may begin in the preclinical stage of AD. We used a naturalistic driving methodology to examine differences in driving behavior over one year in a small sample of cognitively normal older adults with ( n = 10) and without ( n = 10) preclinical AD. As expected with a small sample size, there were no statistically significant differences between the two groups, but older adults with preclinical AD drove less often, were less likely to drive at night, and had fewer aggressive behaviors such as hard braking, speeding, and sudden acceleration. The sample size required to power a larger study to determine differences was calculated.

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology.

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking Device TM was installed in each participant's vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven.  Setting: The Knight Alzheimer's Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance.  Participants were aged 65+ years and had normal cognition. Measurements:  Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations.  Temporal components included number of trips taken during different times of the day.  Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods:  Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS.  Results: Primary locations were confirmed to be 100% accurate when compared to known addresses.  Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns.  Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night.  Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions.